Testing inference in accelerated failure time models

Detalhes bibliográficos
Autor(a) principal: Medeiros, Francisco M. C.
Data de Publicação: 2014
Outros Autores: Silva-Júnior, Antônio H. M. da, Valença, Dione M., Ferrari, Silvia L. P.
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional da UFRN
Texto Completo: https://repositorio.ufrn.br/jspui/handle/123456789/27093
Resumo: We address the issue of performing hypothesis testing in accelerated failure time models for non-censored and censored samples. The performances of the likelihood ratio test and a recently proposed test, the gradient test, are compared through simulation. The gradient test features the same asymptotic properties as the classical large sample tests, namely, the likelihood ratio, Wald and score tests. Additionally, it is as simple to compute as the likelihood ratio test. Unlike the score and Wald tests, the gradient test does require the computation of the information matrix, neither observed nor expected. Our study suggests that the
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spelling Medeiros, Francisco M. C.Silva-Júnior, Antônio H. M. daValença, Dione M.Ferrari, Silvia L. P.2019-05-17T13:13:42Z2019-05-17T13:13:42Z2014-04MEDEIROS, Francisco M. C. et al . Testing inference in accelerated failure time models. International Journal of Statistics and Probability, v. 3, n.2, p. 121-131, 2014. Disponível em: <http://ccsenet.org/journal/index.php/ijsp/article/view/35111>. Acesso em 06 dez. 2017.1927-7040https://repositorio.ufrn.br/jspui/handle/123456789/2709310.5539/ijsp.v3n2p121Canadian Center of Science and EducationAccelerated failure time modelsGradient testLikelihood ratio testRandom censoringTesting inference in accelerated failure time modelsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleWe address the issue of performing hypothesis testing in accelerated failure time models for non-censored and censored samples. The performances of the likelihood ratio test and a recently proposed test, the gradient test, are compared through simulation. The gradient test features the same asymptotic properties as the classical large sample tests, namely, the likelihood ratio, Wald and score tests. Additionally, it is as simple to compute as the likelihood ratio test. Unlike the score and Wald tests, the gradient test does require the computation of the information matrix, neither observed nor expected. Our study suggests that theinfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNTEXTTestingInferenceIn_2014.pdf.txtTestingInferenceIn_2014.pdf.txtExtracted texttext/plain33615https://repositorio.ufrn.br/bitstream/123456789/27093/3/TestingInferenceIn_2014.pdf.txt780ad594fd153b0d80c73f27fdceadf3MD53THUMBNAILTestingInferenceIn_2014.pdf.jpgTestingInferenceIn_2014.pdf.jpgGenerated Thumbnailimage/jpeg1671https://repositorio.ufrn.br/bitstream/123456789/27093/4/TestingInferenceIn_2014.pdf.jpge3eaa4ebfea9706f4683d7ba0c6d1bb2MD54ORIGINALTestingInferenceIn_2014.pdfTestingInferenceIn_2014.pdfapplication/pdf1167548https://repositorio.ufrn.br/bitstream/123456789/27093/1/TestingInferenceIn_2014.pdf5cd33e502df8d50af7a682678dd29a38MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/27093/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/270932019-05-26 03:01:11.031oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2019-05-26T06:01:11Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.pt_BR.fl_str_mv Testing inference in accelerated failure time models
title Testing inference in accelerated failure time models
spellingShingle Testing inference in accelerated failure time models
Medeiros, Francisco M. C.
Accelerated failure time models
Gradient test
Likelihood ratio test
Random censoring
title_short Testing inference in accelerated failure time models
title_full Testing inference in accelerated failure time models
title_fullStr Testing inference in accelerated failure time models
title_full_unstemmed Testing inference in accelerated failure time models
title_sort Testing inference in accelerated failure time models
author Medeiros, Francisco M. C.
author_facet Medeiros, Francisco M. C.
Silva-Júnior, Antônio H. M. da
Valença, Dione M.
Ferrari, Silvia L. P.
author_role author
author2 Silva-Júnior, Antônio H. M. da
Valença, Dione M.
Ferrari, Silvia L. P.
author2_role author
author
author
dc.contributor.author.fl_str_mv Medeiros, Francisco M. C.
Silva-Júnior, Antônio H. M. da
Valença, Dione M.
Ferrari, Silvia L. P.
dc.subject.por.fl_str_mv Accelerated failure time models
Gradient test
Likelihood ratio test
Random censoring
topic Accelerated failure time models
Gradient test
Likelihood ratio test
Random censoring
description We address the issue of performing hypothesis testing in accelerated failure time models for non-censored and censored samples. The performances of the likelihood ratio test and a recently proposed test, the gradient test, are compared through simulation. The gradient test features the same asymptotic properties as the classical large sample tests, namely, the likelihood ratio, Wald and score tests. Additionally, it is as simple to compute as the likelihood ratio test. Unlike the score and Wald tests, the gradient test does require the computation of the information matrix, neither observed nor expected. Our study suggests that the
publishDate 2014
dc.date.issued.fl_str_mv 2014-04
dc.date.accessioned.fl_str_mv 2019-05-17T13:13:42Z
dc.date.available.fl_str_mv 2019-05-17T13:13:42Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv MEDEIROS, Francisco M. C. et al . Testing inference in accelerated failure time models. International Journal of Statistics and Probability, v. 3, n.2, p. 121-131, 2014. Disponível em: <http://ccsenet.org/journal/index.php/ijsp/article/view/35111>. Acesso em 06 dez. 2017.
dc.identifier.uri.fl_str_mv https://repositorio.ufrn.br/jspui/handle/123456789/27093
dc.identifier.issn.none.fl_str_mv 1927-7040
dc.identifier.doi.none.fl_str_mv 10.5539/ijsp.v3n2p121
identifier_str_mv MEDEIROS, Francisco M. C. et al . Testing inference in accelerated failure time models. International Journal of Statistics and Probability, v. 3, n.2, p. 121-131, 2014. Disponível em: <http://ccsenet.org/journal/index.php/ijsp/article/view/35111>. Acesso em 06 dez. 2017.
1927-7040
10.5539/ijsp.v3n2p121
url https://repositorio.ufrn.br/jspui/handle/123456789/27093
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Canadian Center of Science and Education
publisher.none.fl_str_mv Canadian Center of Science and Education
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
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reponame_str Repositório Institucional da UFRN
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